LAB-001
Lead-to-Revenue Engine
A reference implementation showing how inbound leads can be researched, qualified, routed, followed up, and monitored automatically. From web form submission to CRM record, first response, escalation, and reporting.
Reference implementation using synthetic data only. The modeled company is Northstar B2B Systems (fictional). No real client data, client names, or real performance results are presented. All metrics are modeled with stated assumptions.
What the manual version of this workflow looks like
Modeled scenario: Northstar B2B Systems receives ~200 inbound leads per month. Sales team has no automation. This is what their process looks like without it.
Lead arrives by email. Sits unread for hours.
Rep googles the company and pastes notes into a spreadsheet manually
CRM record created manually. Missing fields, inconsistent data across reps.
Manager assigns the lead based on memory of who owns each territory
First response averages 4–8 hours, often the next business day
Follow-up depends entirely on rep discipline. No enforced process.
Manager has no real-time view of which leads were contacted
Missed leads discovered during weekly CRM reviews, retrospectively
What changes after the automation
Lead capture
Form submitted → email lands in the sales inbox → someone checks it later that day, or the next morning.
Research
Sales rep googles the company, checks LinkedIn, and pastes scattered notes into a spreadsheet before CRM entry.
CRM entry
Rep manually creates a contact and company record in HubSpot. Fields are incomplete and data is inconsistent across reps.
Assignment
Sales manager manually decides who gets the lead, usually based on memory of who owns which territory.
First response
Average first response: 4–8 hours. Often the next business day. Qualified leads cool off in the meantime.
Follow-up
Depends entirely on rep discipline. Some follow up three times. Some forget. No consistency across the team.
Visibility
Manager has no real-time view. Weekly CRM check. No alerting. Missed leads discovered in retrospect.
How the system is structured
Eight layers from input to reporting. Click any layer to see what it does and why it is designed that way.
What happens when things go wrong
Most automation demos show the happy path. This shows the failure paths: what each system does when data is invalid, APIs are unavailable, or the AI returns an unexpected result.
Click any scenario to see detection, response, and outcome.
Where humans stay in the loop
Automation does not mean removing human judgment. These are the points where the system deliberately holds for review.
Low-confidence qualification
When AI confidence falls below the defined threshold, the lead is held for human review before any outreach is sent.
High-value accounts
Leads above a defined company size or deal-value threshold require manager approval before automated outreach begins.
Ambiguous AI draft
If the AI-drafted email is flagged for tone or compliance-sensitive language, it is held for human review.
Unclear territory
When territory rules produce no clear match, assignment goes to the sales manager for manual routing.
Exception queue entries
Any lead that fails validation, deduplication, or enrichment past the retry limit lands in a human review queue. Not the bin.
Implementation details
Synthetic environment measurements. Not client production metrics.
12
Pipeline steps
End-to-end workflow nodes
6
Systems connected
n8n, HubSpot, Claude AI, PostgreSQL, Email, Slack
7
Failure scenarios
Each with detection, response, and outcome
~6
API calls per lead
Enrichment, AI, CRM, email, Slack, log
<10s
Demo target
End-to-end in synthetic environment. Actual production time varies.
<$0.05
Estimated AI cost
Per standard demo execution at Haiku pricing. Varies in production.
Modeled time savings
Illustrative model only. Adjust inputs to match your context. Not client data.
Monthly manual hours
200 leads × 13 min ÷ 60
Hours potentially recovered
85% automation assumption
Monthly labor value
At $40/hr
Estimated annual value
Illustrative only · not client results
Reference artifacts
Synthetic representations of what the system produces. All company and person data is fictional.
{
"node": "HTTP Request",
"name": "Enrich Lead",
"parameters": {
"method": "GET",
"url": "https://enrichment.example/v1/company",
"qs": {
"domain": "={{ $json.email.split('@')[1] }}"
},
"retryOnFail": true,
"maxTries": 3,
"waitBetweenTries": 2000
},
"onError": "continueErrorOutput"
}09:04:01 INFO lead.received id=lead_7821 src=webform 09:04:01 INFO validation.passed schema=ok dup=false 09:04:02 INFO enrichment.started domain=northstar-demo.io 09:04:03 INFO enrichment.done employees=47 ind=B2B_SaaS 09:04:03 INFO ai.qualification score=82 confidence=0.91 09:04:03 INFO crm.write.ok contact=hs_29874 owner=sarah_m 09:04:04 INFO email.sent delay=3s status=delivered 09:04:04 INFO slack.notified ch=#sales-us-west 09:04:04 INFO lead.complete total_time=3.1s
New qualified lead — US West
Contact: Jamie Chen — Northstar B2B Systems
Industry: B2B SaaS · 47 employees
ICP Score: 82 / 100 · High fit
First email: Sent (3s ago)
Action: Follow up if no reply by Wed 9am
Synthetic · Northstar B2B Systems is fictional
Discuss a similar workflow
If your team has inbound leads, a CRM, and a follow-up process that still depends on manual work, this pattern is directly applicable.